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The Handbook of NLP with Gensim

You're reading from   The Handbook of NLP with Gensim Leverage topic modeling to uncover hidden patterns, themes, and valuable insights within textual data

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Product type Paperback
Published in Oct 2023
Publisher Packt
ISBN-13 9781803244945
Length 310 pages
Edition 1st Edition
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Author (1):
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Chris Kuo Chris Kuo
Author Profile Icon Chris Kuo
Chris Kuo
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Toc

Table of Contents (24) Chapters Close

Preface 1. Part 1: NLP Basics
2. Chapter 1: Introduction to NLP FREE CHAPTER 3. Chapter 2: Text Representation 4. Chapter 3: Text Wrangling and Preprocessing 5. Part 2: Latent Semantic Analysis/Latent Semantic Indexing
6. Chapter 4: Latent Semantic Analysis with scikit-learn 7. Chapter 5: Cosine Similarity 8. Chapter 6: Latent Semantic Indexing with Gensim 9. Part 3: Word2Vec and Doc2Vec
10. Chapter 7: Using Word2Vec 11. Chapter 8: Doc2Vec with Gensim 12. Part 4: Topic Modeling with Latent Dirichlet Allocation
13. Chapter 9: Understanding Discrete Distributions 14. Chapter 10: Latent Dirichlet Allocation 15. Chapter 11: LDA Modeling 16. Chapter 12: LDA Visualization 17. Chapter 13: The Ensemble LDA for Model Stability 18. Part 5: Comparison and Applications
19. Chapter 14: LDA and BERTopic 20. Chapter 15: Real-World Use Cases 21. Assessments 22. Index 23. Other Books You May Enjoy

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

adaptive radius 217

artificial intelligence (AI) 4

B

Bag-of-N-grams 20

coding 26

using, with NLTK 29, 30

using, with scikit-learn 28, 29

with Gensim 26-28

Bag-of-Visual-Words (BoVW) 21

Bag-of-Words (BoW) 19, 61, 233

coding 22

real-world applications 21

with Gensim 22-24

with scikit-learn 24-26

used, for performing word embedding 79-81

Bayes’ theorem 176, 178

Bernoulli distribution 149

binary outcomes 150

defining 149

example 149

overview 149

BERT-based word embeddings 231

Bidirectional Encoder Representations from Transformers (BERT) 190, 227, 230, 231

BERTopic 227

BERT 231

building 233

c-TFIDF 233

data loading 234

document information, obtaining 237, 239

HDBSCAN 232

keywords of single topic...

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